1. The Field of the Invention
The present invention relates to reducing interference in a data stream, and more particularly, to a system and method for reducing interference by equalizing a data-stream with an adaptive equalizer.
2. Description of the Related Art
Transmission of data over long distances of optical fiber is limited by interference, such as from multimode dispersion, which limits the usable bandwidth of the fiber. Different modes in a multimode fiber travel at different velocities, which can cause a transmitted signal to be unrecoverable without errors by a standard transceiver. This is particularly problematic for fiber optic systems operating at high data rates over long distances—for example, for fiber for 1310 nm light with 10 Gb/s communications over a distance of 300 m. For this type of system, the usable distance may lie in the range of 60 to 100 meters. But, this is far short of the IEEE standard for 10 Gb Ethernet which, specifies a distance of 300 m for multimode fiber.
At an optical receiver on a multimode fiber, the received signal s(t) looks like a convolution of the originally-transmitted signal r(t) and a channel response h(t). The channel response represents the distortion to a pulse caused by the channel (including the transmitter, fiber, and detector), and is assumed to have a finite duration. Thus, if the channel response can be removed or filtered from the received signal, the originally-transmitted signal can be recovered. The quality of a signal can be represented by using an eye diagram. The eye diagram helps to predict bit error rates (BERs). The more open the eye, the lower the BER. Distortion caused by the channel response, including dispersion in multimode fiber, causes the eye to close.
One particular complication of determining the channel response is that it is time variant, with a time constant thought to be one millisecond or greater (but not known exactly). The channel response variation results from interference between different modes of the fiber, and may be caused by, for example, motion of the fiber or thermal variations along the fiber's length. Also, because fibers are often installed in the ceiling of office buildings, they are generally bent around ventilation ducts, which can lead to the coupling of different modes.
One way to reduce interference from dispersion, like multimode fiber dispersion, is by using an equalizer, which works to filter out channel effects in a signal. An equalizer has a response that counteracts at least a portion of the channel effects. Because the channel effects of multimode dispersion are time-varying, an adaptive equalizer that continuously adjusts for the changes in h(t) may be used. One problem of an adaptive equalizer, however, is that it should be initialized with initial filter coefficients to recover the data clock and to converge on the data signal. This generally requires a training sequence (e.g., a sequence of known data) before the start of data communications. Requiring a training sequence presents an interoperability problem, however, because a transmitter in a device might not know it needs to send a training sequence before initiating communications.
One example of an adaptive feedback equalizer is a decision-feedback equalizer (DFE), which filters the incoming signal and compares it to a threshold value to drive the received signal to a high or a low value. A DFE can be implemented in digital logic, analog circuitry, or a hybrid of both. For example, one way to implement a DFE is to place a high speed (e.g. 10 Gb/s) analog-to-digital (A/D) converter in the signal path, followed by a custom-designed digital circuit to perform signal processing. The key disadvantage of this approach is high power consumption—e.g., an A/D converter at this speed typically requires at least one watt of power, and the digital circuitry typically requires another watt. This high-power consumption precludes the use of this approach in most datacom transceivers.
Another approach for implementing a DFE is to use a complete analog solution with no digital circuitry. In this approach, the least mean square (LMS) algorithm can be used to update tap weights in an analog fashion, storing the values on capacitors and using analog multipliers. There are several disadvantages to this approach, however. First, because the amount of analog circuitry required is extensive, the overall design project becomes complex and prone to error. Second, and more intractable, is the problem of false lock, or failure to converge, without the use of a training sequence. And, the use of a training sequence causes the interoperability problem mentioned above.
Finally, there are hybrid analog-digital approaches to DFEs that still utilize the LMS algorithm or similar techniques. For example, a digital circuit can update tap weights for an analog equalizer in the signal path. The digital circuit in this approach would use the LMS algorithm, with updating information obtained from strategically-placed A/D converters. The tap weights are provided back to the analog filter by digital-to-analog (D/A) converters. While this implementation avoids the high-power problems of the pure-digital approach and the design complexity of the pure-analog approach, there is still the vexing problem of obtaining convergence without a training sequence.